{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# pandas 进阶修炼 ｜早起Python\n",
    "<br>\n",
    "\n",
    "**本习题由公众号【早起Python & 可视化图鉴】 原创，转载及其他形式合作请与我们联系（微信号`sshs321`)，未经授权严禁搬运及二次创作，侵权必究！**\n",
    "\n",
    "\n",
    "\n",
    "本习题基于 `pandas` 版本 `1.1.3`，所有内容应当在 `Jupyter Notebook` 中执行以获得最佳效果。\n",
    "\n",
    "不同版本之间写法可能会有少许不同，如若碰到此情况，你应该学会如何自行检索解决。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2 - pandas 个性化显示设置\n",
    "<br>\n",
    "\n",
    "在使用 `pandas` 时，有时默认的配置方案并不能让我们舒服的进行数据分析。\n",
    "\n",
    "幸运的是，`pandas` 也支持我们 <font color=#E36C07>**自定义显示、样式等个性化操作**</font>。\n",
    "\n",
    "本节将部分常用的设置整理为习题形式，<font color=#E36C07>  **所有操作答案拿走即用。既可以刷一遍来了解有这样那样的设置，也可以保存用于速查手册** </font>  \n",
    "\n",
    "注意：本习题中未提及的配置可以点击查阅 `pandas` 👉 [**官方文档对应文章**](https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html)\n",
    "\n",
    "\n",
    " "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 初始化\n",
    "\n",
    "<br>\n",
    "\n",
    "该 `Notebook` 版本为**纯习题版**\n",
    "\n",
    "如果需要答案或者提示，可以微信搜索公众号「早起Python」获取！"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 加载数据"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "导入 `pandas` 并读取当前目录下 `csv` 数据(`data.csv`)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pandas as pd"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "data = pd.read_csv(\"data.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2-1 基于 option 修改显示设置\n",
    "<br>\n",
    "\n",
    "在 `pandas` 中有一个 `option` 系统，可以通过 `set_option`方法进行进阶显示选项设置。\n",
    "\n",
    "本小节主要整理了一些基于 `option` 修改数据显示的设置。\n",
    "\n",
    "注意【**基于 option 修改显示设置**】并未修改数据，仅是在原有数据基础上优化显示状态，随时可以通过重置选项重置全部设置，恢复数据默认显示状态。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1 显示全部列\n",
    "\n",
    "<br>\n",
    "如下图所示👇，直接查看 `data` 会发现，由于数据维度较大，部分行列会被折叠，显示为`...`，现在需要显示全部的列方便预览。\n",
    "\n",
    "![](http://liuzaoqi.oss-cn-beijing.aliyuncs.com/2021/08/19/16293397012946.jpg?域名/sample.jpg?x-oss-process=style/stylename)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在下面的 cell 中输入你的解决方案，并在最后执行 `data.head()`以检查你的答案是否正确解决问题。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "pd.set_option('display.max_columns', None)\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
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       "      <td>['BI', '数据运营']</td>\n",
       "      <td>[]</td>\n",
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       "      <td>全职</td>\n",
       "      <td>大专</td>\n",
       "      <td>五险一金</td>\n",
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       "      <td>2020/3/16 9:56</td>\n",
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       "      <td>120.009765</td>\n",
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       "      <td>20</td>\n",
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       "      <td>12.755375</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>[]</td>\n",
       "      <td>True</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6884346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>21236</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网,医疗丨健康</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['技能培训', '年底双薪', '节日礼物', '绩效奖金']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['数据库', '商业', '数据分析', 'SQL']</td>\n",
       "      <td>['医疗健康', '数据库', '商业', '数据分析', 'SQL']</td>\n",
       "      <td>['医疗健康', '数据库', '商业', '数据分析', 'SQL']</td>\n",
       "      <td>2020/3/11 16:45</td>\n",
       "      <td>2020/3/11</td>\n",
       "      <td>萧山区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25000</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>全职</td>\n",
       "      <td>不限</td>\n",
       "      <td>大牛老板，开放环境，民生行业，龙头公司</td>\n",
       "      <td>threeDays</td>\n",
       "      <td>2020/3/16 9:49</td>\n",
       "      <td>1665167</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.203078</td>\n",
       "      <td>120.247069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.314259</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6849100</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>72076</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['节日礼物', '股票期权', '带薪年假', '年度旅游']</td>\n",
       "      <td>市场|商务类</td>\n",
       "      <td>市场|营销</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>['市场', '数据分析', '行业分析', '市场分析']</td>\n",
       "      <td>['电商', '市场', '数据分析', '行业分析', '市场分析']</td>\n",
       "      <td>['电商', '市场', '数据分析', '行业分析', '市场分析']</td>\n",
       "      <td>2020/3/14 17:38</td>\n",
       "      <td>2天前发布</td>\n",
       "      <td>余杭区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35000</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>全职</td>\n",
       "      <td>硕士</td>\n",
       "      <td>五险一金、带薪休假</td>\n",
       "      <td>threeDays</td>\n",
       "      <td>2020/3/14 17:38</td>\n",
       "      <td>1732416</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.276694</td>\n",
       "      <td>119.990918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.283276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>6803432</td>\n",
       "      <td>奔驰·耀出行-BI数据分析专家</td>\n",
       "      <td>751158</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>[]</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['MySQL', '数据处理', '数据分析']</td>\n",
       "      <td>['MySQL', '数据处理', '数据分析']</td>\n",
       "      <td>[]</td>\n",
       "      <td>2020/3/14 22:39</td>\n",
       "      <td>2天前发布</td>\n",
       "      <td>滨江区</td>\n",
       "      <td>['西兴']</td>\n",
       "      <td>30000</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>全职</td>\n",
       "      <td>本科</td>\n",
       "      <td>奔驰 吉利 世界500强</td>\n",
       "      <td>threeDays</td>\n",
       "      <td>2020/3/14 22:39</td>\n",
       "      <td>4785643</td>\n",
       "      <td>1</td>\n",
       "      <td>1号线</td>\n",
       "      <td>滨和路</td>\n",
       "      <td>1号线_滨和路;1号线_江陵路;1号线_滨和路;1号线_江陵路</td>\n",
       "      <td>30.208562</td>\n",
       "      <td>120.219225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.256719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6704835</td>\n",
       "      <td>BI数据分析师</td>\n",
       "      <td>52840</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>电商</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '年底双薪', '节日礼物', '绩效奖金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['SQLServer', '数据分析']</td>\n",
       "      <td>['电商', '新零售', 'SQLServer', '数据分析']</td>\n",
       "      <td>['电商', '新零售', 'SQLServer', '数据分析']</td>\n",
       "      <td>2020/3/9 15:00</td>\n",
       "      <td>2020/3/9</td>\n",
       "      <td>余杭区</td>\n",
       "      <td>['仓前']</td>\n",
       "      <td>20000</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>全职</td>\n",
       "      <td>本科</td>\n",
       "      <td>阿里巴巴；商业智能；</td>\n",
       "      <td>threeDays</td>\n",
       "      <td>2020/3/16 10:15</td>\n",
       "      <td>5846350</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.280177</td>\n",
       "      <td>120.023521</td>\n",
       "      <td>['16薪', '一年调薪2次']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.281062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6728058</td>\n",
       "      <td>数据分析专家-LQ(J181203029)</td>\n",
       "      <td>2474</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>汽车丨出行</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['弹性工作', '节日礼物', '岗位晋升', '技能培训']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>其他数据分析</td>\n",
       "      <td>[]</td>\n",
       "      <td>['滴滴']</td>\n",
       "      <td>['滴滴']</td>\n",
       "      <td>2020/3/13 18:24</td>\n",
       "      <td>3天前发布</td>\n",
       "      <td>西湖区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21500</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>全职</td>\n",
       "      <td>本科</td>\n",
       "      <td>广阔平台诱人福利</td>\n",
       "      <td>disabled</td>\n",
       "      <td>2020/3/13 19:51</td>\n",
       "      <td>6799495</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.290746</td>\n",
       "      <td>120.074315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.159343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     positionId           positionName  companyId companySize industryField  \\\n",
       "0       6802721                   数据分析     475770     50-150人      移动互联网,电商   \n",
       "1       5204912                   数据建模      50735    150-500人            电商   \n",
       "2       6877668                   数据分析     100125     2000人以上    移动互联网,企业服务   \n",
       "3       6496141                   数据分析      26564   500-2000人            电商   \n",
       "4       6467417                   数据分析      29211     2000人以上         物流丨运输   \n",
       "..          ...                    ...        ...         ...           ...   \n",
       "100     6884346                  数据分析师      21236   500-2000人   移动互联网,医疗丨健康   \n",
       "101     6849100                 商业数据分析      72076   500-2000人      移动互联网,电商   \n",
       "102     6803432        奔驰·耀出行-BI数据分析专家     751158    150-500人         移动互联网   \n",
       "103     6704835                BI数据分析师      52840     2000人以上            电商   \n",
       "104     6728058  数据分析专家-LQ(J181203029)       2474     2000人以上         汽车丨出行   \n",
       "\n",
       "    financeStage                   companyLabelList  firstType secondType  \\\n",
       "0             A轮   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析   \n",
       "1             B轮   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发   \n",
       "2           上市公司   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析   \n",
       "3          D轮及以上  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发   \n",
       "4           上市公司   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析   \n",
       "..           ...                                ...        ...        ...   \n",
       "100           C轮   ['技能培训', '年底双薪', '节日礼物', '绩效奖金']  产品|需求|项目类       数据分析   \n",
       "101           C轮   ['节日礼物', '股票期权', '带薪年假', '年度旅游']     市场|商务类      市场|营销   \n",
       "102        不需要融资                                 []  开发|测试|运维类       数据开发   \n",
       "103         上市公司   ['技能培训', '年底双薪', '节日礼物', '绩效奖金']  开发|测试|运维类       数据开发   \n",
       "104        不需要融资   ['弹性工作', '节日礼物', '岗位晋升', '技能培训']  产品|需求|项目类       数据分析   \n",
       "\n",
       "    thirdType                     skillLables  \\\n",
       "0        数据分析    ['SQL', '数据库', '数据运营', 'BI']   \n",
       "1          建模                  ['算法', '数据架构']   \n",
       "2        数据分析          ['数据库', '数据分析', 'SQL']   \n",
       "3        数据分析                              []   \n",
       "4        数据分析          ['BI', '数据分析', '数据运营']   \n",
       "..        ...                             ...   \n",
       "100      数据分析    ['数据库', '商业', '数据分析', 'SQL']   \n",
       "101    商业数据分析  ['市场', '数据分析', '行业分析', '市场分析']   \n",
       "102      数据分析       ['MySQL', '数据处理', '数据分析']   \n",
       "103      数据分析           ['SQLServer', '数据分析']   \n",
       "104    其他数据分析                              []   \n",
       "\n",
       "                               positionLables  \\\n",
       "0    ['电商', '社交', 'SQL', '数据库', '数据运营', 'BI']   \n",
       "1                              ['算法', '数据架构']   \n",
       "2                              ['数据库', 'SQL']   \n",
       "3                                      ['电商']   \n",
       "4                              ['BI', '数据运营']   \n",
       "..                                        ...   \n",
       "100      ['医疗健康', '数据库', '商业', '数据分析', 'SQL']   \n",
       "101      ['电商', '市场', '数据分析', '行业分析', '市场分析']   \n",
       "102                 ['MySQL', '数据处理', '数据分析']   \n",
       "103        ['电商', '新零售', 'SQLServer', '数据分析']   \n",
       "104                                    ['滴滴']   \n",
       "\n",
       "                               industryLables       createTime  \\\n",
       "0    ['电商', '社交', 'SQL', '数据库', '数据运营', 'BI']  2020/3/16 11:00   \n",
       "1                                          []  2020/3/16 11:08   \n",
       "2                                          []  2020/3/16 10:33   \n",
       "3                                      ['电商']  2020/3/16 10:10   \n",
       "4                                          []   2020/3/16 9:56   \n",
       "..                                        ...              ...   \n",
       "100      ['医疗健康', '数据库', '商业', '数据分析', 'SQL']  2020/3/11 16:45   \n",
       "101      ['电商', '市场', '数据分析', '行业分析', '市场分析']  2020/3/14 17:38   \n",
       "102                                        []  2020/3/14 22:39   \n",
       "103        ['电商', '新零售', 'SQLServer', '数据分析']   2020/3/9 15:00   \n",
       "104                                    ['滴滴']  2020/3/13 18:24   \n",
       "\n",
       "    formatCreateTime district    businessZones  salary workYear jobNature  \\\n",
       "0            11:00发布      余杭区           ['仓前']   37500     1-3年        全职   \n",
       "1            11:08发布      滨江区     ['西兴', '长河']   15000     3-5年        全职   \n",
       "2            10:33发布      江干区  ['四季青', '钱江新城']    3500     1-3年        全职   \n",
       "3            10:10发布      江干区              NaN   45000     3-5年        全职   \n",
       "4            09:56发布      余杭区           ['仓前']   30000     3-5年        全职   \n",
       "..               ...      ...              ...     ...      ...       ...   \n",
       "100        2020/3/11      萧山区              NaN   25000     3-5年        全职   \n",
       "101            2天前发布      余杭区              NaN   35000     1-3年        全职   \n",
       "102            2天前发布      滨江区           ['西兴']   30000     3-5年        全职   \n",
       "103         2020/3/9      余杭区           ['仓前']   20000     3-5年        全职   \n",
       "104            3天前发布      西湖区              NaN   21500    5-10年        全职   \n",
       "\n",
       "    education    positionAdvantage    imState        lastLogin  publisherId  \\\n",
       "0          本科  五险一金、弹性工作、带薪年假、年度体检      today  2020/3/16 11:00     12022406   \n",
       "1          本科       六险一金,定期体检,丰厚年终   disabled  2020/3/16 11:08      5491688   \n",
       "2          本科   五险一金 周末双休 不加班 节日福利      today  2020/3/16 10:33      5322583   \n",
       "3          本科                 年终奖等  threeDays  2020/3/16 10:10      9814560   \n",
       "4          大专                 五险一金   disabled   2020/3/16 9:56      6392394   \n",
       "..        ...                  ...        ...              ...          ...   \n",
       "100        不限  大牛老板，开放环境，民生行业，龙头公司  threeDays   2020/3/16 9:49      1665167   \n",
       "101        硕士            五险一金、带薪休假  threeDays  2020/3/14 17:38      1732416   \n",
       "102        本科         奔驰 吉利 世界500强  threeDays  2020/3/14 22:39      4785643   \n",
       "103        本科           阿里巴巴；商业智能；  threeDays  2020/3/16 10:15      5846350   \n",
       "104        本科             广阔平台诱人福利   disabled  2020/3/13 19:51      6799495   \n",
       "\n",
       "     approve subwayline stationname                       linestaion  \\\n",
       "0          1        NaN         NaN                              NaN   \n",
       "1          1        NaN         NaN                              NaN   \n",
       "2          1        4号线         江锦路         4号线_城星路;4号线_市民中心;4号线_江锦路   \n",
       "3          1        1号线         文泽路                          1号线_文泽路   \n",
       "4          1        NaN         NaN                              NaN   \n",
       "..       ...        ...         ...                              ...   \n",
       "100        1        NaN         NaN                              NaN   \n",
       "101        1        NaN         NaN                              NaN   \n",
       "102        1        1号线         滨和路  1号线_滨和路;1号线_江陵路;1号线_滨和路;1号线_江陵路   \n",
       "103        1        NaN         NaN                              NaN   \n",
       "104        1        NaN         NaN                              NaN   \n",
       "\n",
       "      latitude   longitude             hitags  resumeProcessRate  \\\n",
       "0    30.278421  120.005922                NaN                 50   \n",
       "1    30.188041  120.201179                NaN                 23   \n",
       "2    30.241521  120.212539                NaN                 11   \n",
       "3    30.299404  120.350304                NaN                100   \n",
       "4    30.282952  120.009765                NaN                 20   \n",
       "..         ...         ...                ...                ...   \n",
       "100  30.203078  120.247069                NaN                 96   \n",
       "101  30.276694  119.990918                NaN                  2   \n",
       "102  30.208562  120.219225                NaN                 63   \n",
       "103  30.280177  120.023521  ['16薪', '一年调薪2次']                  0   \n",
       "104  30.290746  120.074315                NaN                  0   \n",
       "\n",
       "     resumeProcessDay  score  newScore  matchScore  matchScoreExplain  query  \\\n",
       "0                   1    233         0   15.101875                NaN    NaN   \n",
       "1                   1    176         0   32.559414                NaN    NaN   \n",
       "2                   4     80         0   14.972357                NaN    NaN   \n",
       "3                   1     68         0   12.874153                NaN    NaN   \n",
       "4                   1     66         0   12.755375                NaN    NaN   \n",
       "..                ...    ...       ...         ...                ...    ...   \n",
       "100                 1      0         0    0.314259                NaN    NaN   \n",
       "101                 3      0         0    0.283276                NaN    NaN   \n",
       "102                 1      0         0    0.256719                NaN    NaN   \n",
       "103                 0      0         0    0.281062                NaN    NaN   \n",
       "104                 0      0         0    0.159343                NaN    NaN   \n",
       "\n",
       "     explain  isSchoolJob  adWord  plus  pcShow  appShow  deliver  \\\n",
       "0        NaN            0       0   NaN       0        0        0   \n",
       "1        NaN            0       0   NaN       0        0        0   \n",
       "2        NaN            0       0   NaN       0        0        0   \n",
       "3        NaN            0       0   NaN       0        0        0   \n",
       "4        NaN            0       0   NaN       0        0        0   \n",
       "..       ...          ...     ...   ...     ...      ...      ...   \n",
       "100      NaN            0       0   NaN       0        0        0   \n",
       "101      NaN            0       0   NaN       0        0        0   \n",
       "102      NaN            0       0   NaN       0        0        0   \n",
       "103      NaN            0       0   NaN       0        0        0   \n",
       "104      NaN            0       0   NaN       0        0        0   \n",
       "\n",
       "     gradeDescription  promotionScoreExplain  isHotHire  count  \\\n",
       "0                 NaN                    NaN          0      0   \n",
       "1                 NaN                    NaN          0      0   \n",
       "2                 NaN                    NaN          0      0   \n",
       "3                 NaN                    NaN          0      0   \n",
       "4                 NaN                    NaN          0      0   \n",
       "..                ...                    ...        ...    ...   \n",
       "100               NaN                    NaN          0      0   \n",
       "101               NaN                    NaN          0      0   \n",
       "102               NaN                    NaN          0      0   \n",
       "103               NaN                    NaN          0      0   \n",
       "104               NaN                    NaN          0      0   \n",
       "\n",
       "    aggregatePositionIds  famousCompany  \n",
       "0                     []          False  \n",
       "1                     []          False  \n",
       "2                     []          False  \n",
       "3                     []           True  \n",
       "4                     []           True  \n",
       "..                   ...            ...  \n",
       "100                   []          False  \n",
       "101                   []          False  \n",
       "102                   []          False  \n",
       "103                   []           True  \n",
       "104                   []           True  \n",
       "\n",
       "[105 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2 显示指定行/列\n",
    "\n",
    "<br>\n",
    "\n",
    "指定让 `data` 在预览时显示10列，7行"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3 还原行/列显示数\n",
    "\n",
    "<br>\n",
    "还原上面的显示设置"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4 修改每列最大字符宽度\n",
    "\n",
    "<br>\n",
    "\n",
    "即每列最多显示的字符长度，例如【每列最多显示10个字符，多余的会变成`...`】"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "pd.set_option('display.max_colwidth', 10)\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
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       "      <th>salary</th>\n",
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       "      <th>education</th>\n",
       "      <th>positionAdvantage</th>\n",
       "      <th>imState</th>\n",
       "      <th>lastLogin</th>\n",
       "      <th>publisherId</th>\n",
       "      <th>approve</th>\n",
       "      <th>subwayline</th>\n",
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       "      <th>newScore</th>\n",
       "      <th>matchScore</th>\n",
       "      <th>matchScoreExplain</th>\n",
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       "      <th>explain</th>\n",
       "      <th>isSchoolJob</th>\n",
       "      <th>adWord</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
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       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
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       "      <th>aggregatePositionIds</th>\n",
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       "  </thead>\n",
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       "      <td>6802721</td>\n",
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       "      <td>2020/3...</td>\n",
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       "      <td>today</td>\n",
       "      <td>2020/3...</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.278421</td>\n",
       "      <td>120.00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>15.101875</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>5204912</td>\n",
       "      <td>数据建模</td>\n",
       "      <td>50735</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
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       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
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       "      <td>['算法',...</td>\n",
       "      <td>['算法',...</td>\n",
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       "      <td>2020/3...</td>\n",
       "      <td>11:08发布</td>\n",
       "      <td>滨江区</td>\n",
       "      <td>['西兴',...</td>\n",
       "      <td>15000</td>\n",
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       "      <td>六险一金,定...</td>\n",
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       "      <td>2020/3...</td>\n",
       "      <td>5491688</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.188041</td>\n",
       "      <td>120.20...</td>\n",
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       "      <td>23</td>\n",
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       "      <td>32.559414</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>100125</td>\n",
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       "      <td>2020/3...</td>\n",
       "      <td>10:33发布</td>\n",
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       "      <td>['四季青'...</td>\n",
       "      <td>3500</td>\n",
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       "      <td>全职</td>\n",
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       "      <td>五险一金 周...</td>\n",
       "      <td>today</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>5322583</td>\n",
       "      <td>1</td>\n",
       "      <td>4号线</td>\n",
       "      <td>江锦路</td>\n",
       "      <td>4号线_城星...</td>\n",
       "      <td>30.241521</td>\n",
       "      <td>120.21...</td>\n",
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       "      <td>11</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>26564</td>\n",
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       "      <td>['生日趴'...</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
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       "      <td>['电商']</td>\n",
       "      <td>['电商']</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>10:10发布</td>\n",
       "      <td>江干区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45000</td>\n",
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       "      <td>年终奖等</td>\n",
       "      <td>threeDays</td>\n",
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       "      <td>9814560</td>\n",
       "      <td>1</td>\n",
       "      <td>1号线</td>\n",
       "      <td>文泽路</td>\n",
       "      <td>1号线_文泽路</td>\n",
       "      <td>30.299404</td>\n",
       "      <td>120.35...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>12.874153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训...</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['BI',...</td>\n",
       "      <td>['BI',...</td>\n",
       "      <td>[]</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>09:56发布</td>\n",
       "      <td>余杭区</td>\n",
       "      <td>['仓前']</td>\n",
       "      <td>30000</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>全职</td>\n",
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       "      <td>五险一金</td>\n",
       "      <td>disabled</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>6392394</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.282952</td>\n",
       "      <td>120.00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>True</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6884346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>21236</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网,...</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['技能培训...</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['数据库'...</td>\n",
       "      <td>['医疗健康...</td>\n",
       "      <td>['医疗健康...</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>2020/3/11</td>\n",
       "      <td>萧山区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25000</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>全职</td>\n",
       "      <td>不限</td>\n",
       "      <td>大牛老板，开...</td>\n",
       "      <td>threeDays</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>1665167</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.203078</td>\n",
       "      <td>120.24...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.314259</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6849100</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>72076</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['节日礼物...</td>\n",
       "      <td>市场|商务类</td>\n",
       "      <td>市场|营销</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>['市场',...</td>\n",
       "      <td>['电商',...</td>\n",
       "      <td>['电商',...</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>2天前发布</td>\n",
       "      <td>余杭区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35000</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>全职</td>\n",
       "      <td>硕士</td>\n",
       "      <td>五险一金、带薪休假</td>\n",
       "      <td>threeDays</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>1732416</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.276694</td>\n",
       "      <td>119.99...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.283276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>6803432</td>\n",
       "      <td>奔驰·耀出行...</td>\n",
       "      <td>751158</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>[]</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['MySQ...</td>\n",
       "      <td>['MySQ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>2天前发布</td>\n",
       "      <td>滨江区</td>\n",
       "      <td>['西兴']</td>\n",
       "      <td>30000</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>全职</td>\n",
       "      <td>本科</td>\n",
       "      <td>奔驰 吉利 ...</td>\n",
       "      <td>threeDays</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>4785643</td>\n",
       "      <td>1</td>\n",
       "      <td>1号线</td>\n",
       "      <td>滨和路</td>\n",
       "      <td>1号线_滨和...</td>\n",
       "      <td>30.208562</td>\n",
       "      <td>120.21...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.256719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6704835</td>\n",
       "      <td>BI数据分析师</td>\n",
       "      <td>52840</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>电商</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训...</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['SQLS...</td>\n",
       "      <td>['电商',...</td>\n",
       "      <td>['电商',...</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>2020/3/9</td>\n",
       "      <td>余杭区</td>\n",
       "      <td>['仓前']</td>\n",
       "      <td>20000</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>全职</td>\n",
       "      <td>本科</td>\n",
       "      <td>阿里巴巴；商...</td>\n",
       "      <td>threeDays</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>5846350</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.280177</td>\n",
       "      <td>120.02...</td>\n",
       "      <td>['16薪'...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.281062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6728058</td>\n",
       "      <td>数据分析专家...</td>\n",
       "      <td>2474</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>汽车丨出行</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['弹性工作...</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>其他数据分析</td>\n",
       "      <td>[]</td>\n",
       "      <td>['滴滴']</td>\n",
       "      <td>['滴滴']</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>3天前发布</td>\n",
       "      <td>西湖区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21500</td>\n",
       "      <td>5-10年</td>\n",
       "      <td>全职</td>\n",
       "      <td>本科</td>\n",
       "      <td>广阔平台诱人福利</td>\n",
       "      <td>disabled</td>\n",
       "      <td>2020/3...</td>\n",
       "      <td>6799495</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.290746</td>\n",
       "      <td>120.07...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.159343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     positionId positionName  companyId companySize industryField  \\\n",
       "0      6802721        数据分析       475770    50-150人    移动互联网,电商      \n",
       "1      5204912        数据建模        50735   150-500人          电商      \n",
       "2      6877668        数据分析       100125    2000人以上   移动互联网,...      \n",
       "3      6496141        数据分析        26564  500-2000人          电商      \n",
       "4      6467417        数据分析        29211    2000人以上       物流丨运输      \n",
       "..         ...         ...          ...        ...         ...      \n",
       "100    6884346       数据分析师        21236  500-2000人   移动互联网,...      \n",
       "101    6849100      商业数据分析        72076  500-2000人    移动互联网,电商      \n",
       "102    6803432   奔驰·耀出行...       751158   150-500人       移动互联网      \n",
       "103    6704835     BI数据分析师        52840    2000人以上          电商      \n",
       "104    6728058   数据分析专家...         2474    2000人以上       汽车丨出行      \n",
       "\n",
       "    financeStage companyLabelList  firstType secondType thirdType skillLables  \\\n",
       "0           A轮    ['绩效奖金...        产品|需求|项目类       数据分析      数据分析  ['SQL'...    \n",
       "1           B轮    ['年终奖金...        开发|测试|运维类       数据开发        建模  ['算法',...    \n",
       "2         上市公司    ['节日礼物...        产品|需求|项目类       数据分析      数据分析  ['数据库'...    \n",
       "3        D轮及以上    ['生日趴'...        开发|测试|运维类       数据开发      数据分析         []    \n",
       "4         上市公司    ['技能培训...        产品|需求|项目类       数据分析      数据分析  ['BI',...    \n",
       "..         ...          ...              ...        ...       ...        ...    \n",
       "100         C轮    ['技能培训...        产品|需求|项目类       数据分析      数据分析  ['数据库'...    \n",
       "101         C轮    ['节日礼物...           市场|商务类      市场|营销    商业数据分析  ['市场',...    \n",
       "102      不需要融资           []        开发|测试|运维类       数据开发      数据分析  ['MySQ...    \n",
       "103       上市公司    ['技能培训...        开发|测试|运维类       数据开发      数据分析  ['SQLS...    \n",
       "104      不需要融资    ['弹性工作...        产品|需求|项目类       数据分析    其他数据分析         []    \n",
       "\n",
       "    positionLables industryLables createTime formatCreateTime district  \\\n",
       "0    ['电商',...      ['电商',...      2020/3...    11:00发布            余杭区   \n",
       "1    ['算法',...             []      2020/3...    11:08发布            滨江区   \n",
       "2    ['数据库'...             []      2020/3...    10:33发布            江干区   \n",
       "3       ['电商']         ['电商']      2020/3...    10:10发布            江干区   \n",
       "4    ['BI',...             []      2020/3...    09:56发布            余杭区   \n",
       "..         ...            ...            ...        ...            ...   \n",
       "100  ['医疗健康...      ['医疗健康...      2020/3...  2020/3/11            萧山区   \n",
       "101  ['电商',...      ['电商',...      2020/3...      2天前发布            余杭区   \n",
       "102  ['MySQ...             []      2020/3...      2天前发布            滨江区   \n",
       "103  ['电商',...      ['电商',...      2020/3...   2020/3/9            余杭区   \n",
       "104     ['滴滴']         ['滴滴']      2020/3...      3天前发布            西湖区   \n",
       "\n",
       "    businessZones  salary workYear jobNature education positionAdvantage  \\\n",
       "0       ['仓前']      37500     1-3年        全职        本科  五险一金、弹...          \n",
       "1    ['西兴',...      15000     3-5年        全职        本科  六险一金,定...          \n",
       "2    ['四季青'...       3500     1-3年        全职        本科  五险一金 周...          \n",
       "3          NaN      45000     3-5年        全职        本科       年终奖等          \n",
       "4       ['仓前']      30000     3-5年        全职        大专       五险一金          \n",
       "..         ...        ...      ...       ...       ...        ...          \n",
       "100        NaN      25000     3-5年        全职        不限  大牛老板，开...          \n",
       "101        NaN      35000     1-3年        全职        硕士  五险一金、带薪休假          \n",
       "102     ['西兴']      30000     3-5年        全职        本科  奔驰 吉利 ...          \n",
       "103     ['仓前']      20000     3-5年        全职        本科  阿里巴巴；商...          \n",
       "104        NaN      21500    5-10年        全职        本科   广阔平台诱人福利          \n",
       "\n",
       "       imState  lastLogin  publisherId  approve subwayline stationname  \\\n",
       "0        today  2020/3...   12022406          1        NaN        NaN    \n",
       "1     disabled  2020/3...    5491688          1        NaN        NaN    \n",
       "2        today  2020/3...    5322583          1        4号线        江锦路    \n",
       "3    threeDays  2020/3...    9814560          1        1号线        文泽路    \n",
       "4     disabled  2020/3...    6392394          1        NaN        NaN    \n",
       "..         ...        ...        ...        ...        ...        ...    \n",
       "100  threeDays  2020/3...    1665167          1        NaN        NaN    \n",
       "101  threeDays  2020/3...    1732416          1        NaN        NaN    \n",
       "102  threeDays  2020/3...    4785643          1        1号线        滨和路    \n",
       "103  threeDays  2020/3...    5846350          1        NaN        NaN    \n",
       "104   disabled  2020/3...    6799495          1        NaN        NaN    \n",
       "\n",
       "    linestaion   latitude  longitude     hitags  resumeProcessRate  \\\n",
       "0          NaN  30.278421  120.00...        NaN         50           \n",
       "1          NaN  30.188041  120.20...        NaN         23           \n",
       "2    4号线_城星...  30.241521  120.21...        NaN         11           \n",
       "3      1号线_文泽路  30.299404  120.35...        NaN        100           \n",
       "4          NaN  30.282952  120.00...        NaN         20           \n",
       "..         ...        ...        ...        ...        ...           \n",
       "100        NaN  30.203078  120.24...        NaN         96           \n",
       "101        NaN  30.276694  119.99...        NaN          2           \n",
       "102  1号线_滨和...  30.208562  120.21...        NaN         63           \n",
       "103        NaN  30.280177  120.02...  ['16薪'...          0           \n",
       "104        NaN  30.290746  120.07...        NaN          0           \n",
       "\n",
       "     resumeProcessDay  score  newScore  matchScore  matchScoreExplain  query  \\\n",
       "0            1           233         0  15.101875         NaN            NaN   \n",
       "1            1           176         0  32.559414         NaN            NaN   \n",
       "2            4            80         0  14.972357         NaN            NaN   \n",
       "3            1            68         0  12.874153         NaN            NaN   \n",
       "4            1            66         0  12.755375         NaN            NaN   \n",
       "..         ...           ...       ...        ...         ...            ...   \n",
       "100          1             0         0   0.314259         NaN            NaN   \n",
       "101          3             0         0   0.283276         NaN            NaN   \n",
       "102          1             0         0   0.256719         NaN            NaN   \n",
       "103          0             0         0   0.281062         NaN            NaN   \n",
       "104          0             0         0   0.159343         NaN            NaN   \n",
       "\n",
       "     explain  isSchoolJob  adWord  plus  pcShow  appShow  deliver  \\\n",
       "0        NaN          0         0   NaN       0        0        0   \n",
       "1        NaN          0         0   NaN       0        0        0   \n",
       "2        NaN          0         0   NaN       0        0        0   \n",
       "3        NaN          0         0   NaN       0        0        0   \n",
       "4        NaN          0         0   NaN       0        0        0   \n",
       "..       ...        ...       ...   ...     ...      ...      ...   \n",
       "100      NaN          0         0   NaN       0        0        0   \n",
       "101      NaN          0         0   NaN       0        0        0   \n",
       "102      NaN          0         0   NaN       0        0        0   \n",
       "103      NaN          0         0   NaN       0        0        0   \n",
       "104      NaN          0         0   NaN       0        0        0   \n",
       "\n",
       "     gradeDescription  promotionScoreExplain  isHotHire  count  \\\n",
       "0          NaN               NaN                      0      0   \n",
       "1          NaN               NaN                      0      0   \n",
       "2          NaN               NaN                      0      0   \n",
       "3          NaN               NaN                      0      0   \n",
       "4          NaN               NaN                      0      0   \n",
       "..         ...               ...                    ...    ...   \n",
       "100        NaN               NaN                      0      0   \n",
       "101        NaN               NaN                      0      0   \n",
       "102        NaN               NaN                      0      0   \n",
       "103        NaN               NaN                      0      0   \n",
       "104        NaN               NaN                      0      0   \n",
       "\n",
       "    aggregatePositionIds  famousCompany  \n",
       "0           []                False      \n",
       "1           []                False      \n",
       "2           []                False      \n",
       "3           []                 True      \n",
       "4           []                 True      \n",
       "..         ...                  ...      \n",
       "100         []                False      \n",
       "101         []                False      \n",
       "102         []                False      \n",
       "103         []                 True      \n",
       "104         []                 True      \n",
       "\n",
       "[105 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {
    "scrolled": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 5 修改小数点精度\n",
    "\n",
    "<br>\n",
    "\n",
    "修改默认显示精度为小数点后5位"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 6 还原所有显示设置"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "还原上面的全部显示设置"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2-2 更多 option 相关设置"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 7 忽略警告\n",
    "<br>\n",
    "\n",
    "取消`pandas`相关`warning`提示"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 8 设置数值显示条件"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "如果数值小于 20 则显示为0"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 9 让 pandas 支持 LaTex"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "让`dataframe`中内容支持 `Latex` 显示（需要使用`$$`包住）"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 10 修改默认绘图引擎\n",
    "\n",
    "修改`pandas`默认绘图引擎为`plotly`（需要提前安装好`plotly`）"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 11 还原所有 option 设置"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "还原上面全部 option 设置"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 彩蛋"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "如何设置在预览数据时，不换行显示每列内容？\n",
    "\n",
    "![](http://liuzaoqi.oss-cn-beijing.aliyuncs.com/2021/08/19/16293394983242.jpg?域名/sample.jpg?x-oss-process=style/stylename)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2-3 基于 style  个性化设置"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "上面基于 `option` 的 `pandas` 相关设置是<font color=#E36C07>全局配置</font>，一次设置会在关闭notebook前一直有效\n",
    "\n",
    "但相关常用的设置并不多，不能满足更多的个性化需求。\n",
    "\n",
    "幸运的是在 `pandas` 中提供 `Styler` 对象让我们进一步个性化展示数据。\n",
    "\n",
    "本节我就将一些常用的基于 `style` 个性化设置整理为习题模式方便大家学习、巩固。\n",
    "\n",
    "注意：基于 `style` 个性化设置<font color=#E36C07>**同样不会修改数据**</font>，所有 `data.style.xxxx` 输出的数据均是<font color=#E36C07>一次性的（可以复用、导出）</font>，因此你应该在合适的时间选择使用该方法。\n",
    "\n",
    "下面仅列举常用的方法，若想了解更多可以查阅[**pandas官方文档对应文章👉**](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  重新加载数据"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "为了方便理解，重新读取`data.csv`**前20行指定列**\n",
    "- `'positionName'`\n",
    "- `'createTime'`（设置为时间格式）\n",
    "- `'salary'`\n",
    "- `'subwayline'`\n",
    "- `'matchScore'`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "data = pd.read_csv(\"data.csv\", usecols=[\r\n",
    "                   'positionName', 'createTime', 'salary', 'subwayline', 'matchScore'], nrows=20, parse_dates=['createTime'])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 12 隐藏索引\n",
    "\n",
    "<br>\n",
    "\n",
    "隐藏索引列"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 13 调整精度\n",
    "\n",
    "<br>\n",
    "\n",
    "将带有小数点的列精度调整为小数点后2位"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 14 标记缺失值"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "将缺失值标记为`数据缺失`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "data.fillna('数据缺失', inplace=True)\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionName</th>\n",
       "      <th>createTime</th>\n",
       "      <th>salary</th>\n",
       "      <th>subwayline</th>\n",
       "      <th>matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>37500</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据建模</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>15000</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>3500</td>\n",
       "      <td>4号线</td>\n",
       "      <td>14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>45000</td>\n",
       "      <td>1号线</td>\n",
       "      <td>12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>30000</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>50000</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>30000</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>数据建模工程师</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>35000</td>\n",
       "      <td>2号线</td>\n",
       "      <td>3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>60000</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>40000</td>\n",
       "      <td>2号线</td>\n",
       "      <td>1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>30000</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>大数据分析工...</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>30000</td>\n",
       "      <td>2号线</td>\n",
       "      <td>4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>20000</td>\n",
       "      <td>2号线</td>\n",
       "      <td>1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>30000</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>37500</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>产品运营（偏...</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>27500</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>资深数据分析...</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>37500</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>大数据建模总监</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>37500</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>数据建模专家...</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>30000</td>\n",
       "      <td>数据缺失</td>\n",
       "      <td>3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>数据分析专家...</td>\n",
       "      <td>2020-03...</td>\n",
       "      <td>37500</td>\n",
       "      <td>2号线</td>\n",
       "      <td>0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   positionName createTime  salary subwayline  matchScore\n",
       "0        数据分析   2020-03...   37500       数据缺失  15.101875 \n",
       "1        数据建模   2020-03...   15000       数据缺失  32.559414 \n",
       "2        数据分析   2020-03...    3500        4号线  14.972357 \n",
       "3        数据分析   2020-03...   45000        1号线  12.874153 \n",
       "4        数据分析   2020-03...   30000       数据缺失  12.755375 \n",
       "5        数据分析   2020-03...   50000       数据缺失  12.718732 \n",
       "6        数据分析   2020-03...   30000       数据缺失  12.615116 \n",
       "7     数据建模工程师   2020-03...   35000        2号线   3.033237 \n",
       "8      数据分析专家   2020-03...   60000       数据缺失   1.141952 \n",
       "9       数据分析师   2020-03...   40000        2号线   1.177361 \n",
       "10      数据分析师   2020-03...   30000       数据缺失   1.161869 \n",
       "11  大数据分析工...   2020-03...   30000        2号线   4.245066 \n",
       "12    数据分析工程师   2020-03...   20000        2号线   1.091051 \n",
       "13    资深数据分析师   2020-03...   30000       数据缺失   1.075559 \n",
       "14      数据分析师   2020-03...   37500       数据缺失   1.053428 \n",
       "15  产品运营（偏...   2020-03...   27500       数据缺失   1.015806 \n",
       "16  资深数据分析...   2020-03...   37500       数据缺失   1.009167 \n",
       "17    大数据建模总监   2020-03...   37500       数据缺失   2.719454 \n",
       "18  数据建模专家...   2020-03...   30000       数据缺失   3.033237 \n",
       "19  数据分析专家...   2020-03...   37500        2号线   0.834333 "
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "微信搜索公众号「早起Python」，关注后可以获得更多资源！"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 15 高亮缺失值\n",
    "\n",
    "<br>\n",
    "\n",
    "将缺失值高亮，颜色名`skyblue`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "data.style.highlight_null(null_color=\"skyblue\")\r\n",
    "#data.style.highlight_between(subset=\"salary\", left= 40000)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_727d2_row0_col3, #T_727d2_row1_col3, #T_727d2_row4_col3, #T_727d2_row5_col3, #T_727d2_row6_col3, #T_727d2_row8_col3, #T_727d2_row10_col3, #T_727d2_row13_col3, #T_727d2_row14_col3, #T_727d2_row15_col3, #T_727d2_row16_col3, #T_727d2_row17_col3, #T_727d2_row18_col3 {\n",
       "  background-color: skyblue;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_727d2_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_727d2_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_727d2_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_727d2_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_727d2_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_727d2_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_727d2_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_727d2_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_727d2_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_727d2_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_727d2_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_727d2_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_727d2_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_727d2_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_727d2_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_727d2_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_727d2_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_727d2_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_727d2_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_727d2_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_727d2_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_727d2_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_727d2_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_727d2_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_727d2_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_727d2_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_727d2_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_727d2_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_727d2_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_727d2_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_727d2_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_727d2_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_727d2_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_727d2_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_727d2_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_727d2_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_727d2_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_727d2_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_727d2_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_727d2_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_727d2_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_727d2_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_727d2_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_727d2_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_727d2_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_727d2_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_727d2_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_727d2_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_727d2_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_727d2_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_727d2_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_727d2_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_727d2_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_727d2_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_727d2_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_727d2_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_727d2_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_727d2_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_727d2_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_727d2_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_727d2_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_727d2_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_727d2_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_727d2_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_727d2_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_727d2_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_727d2_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_727d2_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_727d2_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_727d2_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_727d2_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_727d2_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_727d2_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_727d2_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_727d2_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_727d2_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_727d2_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_727d2_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_727d2_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_727d2_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_727d2_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_727d2_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_727d2_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_727d2_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_727d2_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_727d2_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_727d2_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_727d2_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_727d2_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_727d2_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a49362b9a0>"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 16 高亮数值列最大值\n",
    "<br>\n",
    "将 数值格式列的最大值进行高亮"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "data.style.highlight_max(subset=['salary', 'matchScore'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6be70_row1_col4, #T_6be70_row8_col2 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6be70_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_6be70_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_6be70_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_6be70_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_6be70_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_6be70_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_6be70_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_6be70_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_6be70_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_6be70_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_6be70_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_6be70_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_6be70_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_6be70_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_6be70_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_6be70_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_6be70_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_6be70_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_6be70_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_6be70_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_6be70_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_6be70_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_6be70_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_6be70_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_6be70_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_6be70_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_6be70_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_6be70_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_6be70_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_6be70_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_6be70_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_6be70_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_6be70_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_6be70_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_6be70_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_6be70_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_6be70_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_6be70_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_6be70_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_6be70_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_6be70_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_6be70_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_6be70_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_6be70_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_6be70_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_6be70_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_6be70_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_6be70_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_6be70_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_6be70_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_6be70_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_6be70_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_6be70_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_6be70_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_6be70_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_6be70_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_6be70_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_6be70_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_6be70_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_6be70_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_6be70_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_6be70_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_6be70_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_6be70_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_6be70_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_6be70_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_6be70_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_6be70_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_6be70_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_6be70_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_6be70_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_6be70_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_6be70_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_6be70_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_6be70_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_6be70_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_6be70_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_6be70_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_6be70_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_6be70_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_6be70_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_6be70_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_6be70_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_6be70_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6be70_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_6be70_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_6be70_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_6be70_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_6be70_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_6be70_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a49362b220>"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 17 高亮数值列最小值\n",
    "<br>\n",
    "将 数值格式列的最小值进行高亮"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "data.style.highlight_min(subset=['salary','matchScore'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a7364_row2_col2, #T_a7364_row19_col4 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a7364_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_a7364_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_a7364_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_a7364_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_a7364_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_a7364_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_a7364_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_a7364_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_a7364_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_a7364_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_a7364_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_a7364_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_a7364_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_a7364_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_a7364_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_a7364_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_a7364_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_a7364_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_a7364_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_a7364_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_a7364_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_a7364_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_a7364_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_a7364_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_a7364_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_a7364_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_a7364_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_a7364_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_a7364_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_a7364_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_a7364_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_a7364_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_a7364_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_a7364_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_a7364_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_a7364_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_a7364_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_a7364_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_a7364_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_a7364_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_a7364_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_a7364_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_a7364_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_a7364_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_a7364_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_a7364_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_a7364_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_a7364_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_a7364_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_a7364_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_a7364_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_a7364_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_a7364_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_a7364_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_a7364_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_a7364_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_a7364_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_a7364_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_a7364_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_a7364_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_a7364_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_a7364_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_a7364_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_a7364_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_a7364_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_a7364_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_a7364_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_a7364_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_a7364_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_a7364_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_a7364_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_a7364_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_a7364_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_a7364_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_a7364_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_a7364_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_a7364_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_a7364_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_a7364_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_a7364_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_a7364_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_a7364_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_a7364_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_a7364_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7364_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_a7364_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_a7364_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_a7364_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_a7364_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_a7364_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a49362bee0>"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 18 同时高亮最大最小值\r\n",
    "\r\n",
    "<br>\r\n",
    "\r\n",
    "同时高亮最大值（颜色代码为`#F77802`）与最小值（颜色代码为`#26BE49`）"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "(data.style\r\n",
    "    .highlight_min(subset=['salary','matchScore'], color=\"#26BE49\")\r\n",
    "    .highlight_max(subset=['salary','matchScore'], color=\"#F77802\")\r\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_2fa2b_row1_col4, #T_2fa2b_row8_col2 {\n",
       "  background-color: #F77802;\n",
       "}\n",
       "#T_2fa2b_row2_col2, #T_2fa2b_row19_col4 {\n",
       "  background-color: #26BE49;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2fa2b_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_2fa2b_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_2fa2b_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_2fa2b_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_2fa2b_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_2fa2b_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_2fa2b_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_2fa2b_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_2fa2b_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_2fa2b_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_2fa2b_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_2fa2b_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_2fa2b_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_2fa2b_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_2fa2b_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_2fa2b_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_2fa2b_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_2fa2b_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_2fa2b_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_2fa2b_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_2fa2b_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_2fa2b_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_2fa2b_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_2fa2b_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_2fa2b_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_2fa2b_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_2fa2b_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_2fa2b_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_2fa2b_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_2fa2b_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_2fa2b_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_2fa2b_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_2fa2b_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_2fa2b_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_2fa2b_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_2fa2b_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_2fa2b_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_2fa2b_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_2fa2b_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_2fa2b_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_2fa2b_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2fa2b_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_2fa2b_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_2fa2b_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_2fa2b_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_2fa2b_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2fa2b_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_2fa2b_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_2fa2b_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_2fa2b_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_2fa2b_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_2fa2b_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_2fa2b_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_2fa2b_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_2fa2b_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_2fa2b_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_2fa2b_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_2fa2b_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_2fa2b_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_2fa2b_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_2fa2b_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_2fa2b_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_2fa2b_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_2fa2b_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2fa2b_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_2fa2b_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_2fa2b_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_2fa2b_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_2fa2b_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_2fa2b_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_2fa2b_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_2fa2b_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_2fa2b_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_2fa2b_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_2fa2b_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_2fa2b_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_2fa2b_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_2fa2b_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_2fa2b_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_2fa2b_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_2fa2b_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_2fa2b_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_2fa2b_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_2fa2b_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2fa2b_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_2fa2b_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_2fa2b_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_2fa2b_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_2fa2b_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_2fa2b_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a49362b340>"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 19 指定格式高亮"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "高亮 `salary` 列范围在 3000 - 10000 的数值"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "data.style.highlight_between(subset='salary', color='#f77802', left=3000, right=20000)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_8139c_row1_col2, #T_8139c_row2_col2, #T_8139c_row12_col2 {\n",
       "  background-color: #f77802;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8139c_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8139c_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_8139c_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_8139c_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_8139c_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_8139c_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_8139c_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_8139c_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_8139c_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_8139c_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_8139c_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_8139c_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_8139c_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_8139c_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_8139c_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_8139c_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_8139c_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_8139c_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_8139c_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_8139c_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_8139c_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_8139c_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_8139c_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_8139c_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_8139c_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_8139c_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_8139c_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_8139c_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_8139c_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_8139c_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_8139c_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_8139c_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_8139c_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_8139c_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_8139c_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_8139c_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_8139c_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_8139c_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_8139c_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_8139c_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_8139c_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_8139c_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_8139c_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_8139c_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_8139c_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_8139c_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_8139c_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_8139c_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_8139c_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_8139c_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_8139c_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_8139c_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_8139c_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_8139c_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_8139c_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_8139c_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_8139c_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_8139c_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_8139c_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_8139c_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_8139c_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_8139c_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_8139c_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_8139c_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_8139c_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_8139c_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_8139c_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_8139c_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_8139c_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_8139c_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_8139c_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_8139c_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_8139c_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_8139c_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_8139c_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_8139c_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_8139c_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_8139c_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_8139c_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_8139c_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_8139c_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_8139c_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_8139c_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_8139c_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8139c_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_8139c_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_8139c_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_8139c_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_8139c_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_8139c_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a4929886d0>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 20 渐变显示数值列"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "将数值格式的列使用渐变色（绿色）进行显示，以突出趋势"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "import seaborn as sns\r\n",
    "\r\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)\r\n",
    "\r\n",
    "(data.style\r\n",
    "    .background_gradient(cmap=cm,subset=['salary', 'matchScore'])\r\n",
    "    .highlight_max(subset=['salary','matchScore'], color=\"#F77802\")\r\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_00c2a_row0_col2, #T_00c2a_row14_col2, #T_00c2a_row16_col2, #T_00c2a_row17_col2, #T_00c2a_row19_col2 {\n",
       "  background-color: #5dae5d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00c2a_row0_col4 {\n",
       "  background-color: #81bf81;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row1_col2 {\n",
       "  background-color: #bbdcbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row1_col4, #T_00c2a_row8_col2 {\n",
       "  background-color: #008000;\n",
       "  color: #f1f1f1;\n",
       "  background-color: #F77802;\n",
       "}\n",
       "#T_00c2a_row2_col2, #T_00c2a_row19_col4 {\n",
       "  background-color: #ebf3eb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row2_col4 {\n",
       "  background-color: #82c082;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row3_col2 {\n",
       "  background-color: #3e9e3e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00c2a_row3_col4 {\n",
       "  background-color: #91c791;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row4_col2, #T_00c2a_row6_col2, #T_00c2a_row10_col2, #T_00c2a_row11_col2, #T_00c2a_row13_col2, #T_00c2a_row18_col2 {\n",
       "  background-color: #7cbd7c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row4_col4 {\n",
       "  background-color: #92c892;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row5_col2 {\n",
       "  background-color: #299429;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00c2a_row5_col4, #T_00c2a_row6_col4 {\n",
       "  background-color: #93c893;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row7_col2 {\n",
       "  background-color: #68b368;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00c2a_row7_col4, #T_00c2a_row18_col4 {\n",
       "  background-color: #dbebdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row8_col4, #T_00c2a_row9_col4, #T_00c2a_row10_col4, #T_00c2a_row12_col4 {\n",
       "  background-color: #e9f2e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row9_col2 {\n",
       "  background-color: #53a953;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00c2a_row11_col4 {\n",
       "  background-color: #d2e7d2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row12_col2 {\n",
       "  background-color: #a6d2a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row13_col4, #T_00c2a_row14_col4, #T_00c2a_row15_col4, #T_00c2a_row16_col4 {\n",
       "  background-color: #eaf2ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row15_col2 {\n",
       "  background-color: #87c287;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_00c2a_row17_col4 {\n",
       "  background-color: #ddecdd;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_00c2a_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_00c2a_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_00c2a_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_00c2a_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_00c2a_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_00c2a_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_00c2a_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_00c2a_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_00c2a_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_00c2a_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_00c2a_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_00c2a_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_00c2a_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_00c2a_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_00c2a_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_00c2a_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_00c2a_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_00c2a_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_00c2a_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_00c2a_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_00c2a_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_00c2a_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_00c2a_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_00c2a_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_00c2a_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_00c2a_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_00c2a_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_00c2a_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_00c2a_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_00c2a_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_00c2a_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_00c2a_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_00c2a_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_00c2a_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_00c2a_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_00c2a_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_00c2a_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_00c2a_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_00c2a_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_00c2a_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_00c2a_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_00c2a_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_00c2a_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_00c2a_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_00c2a_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_00c2a_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_00c2a_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_00c2a_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_00c2a_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_00c2a_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_00c2a_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_00c2a_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_00c2a_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_00c2a_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_00c2a_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_00c2a_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_00c2a_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_00c2a_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_00c2a_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_00c2a_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_00c2a_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_00c2a_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_00c2a_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_00c2a_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_00c2a_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_00c2a_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_00c2a_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_00c2a_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_00c2a_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_00c2a_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_00c2a_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_00c2a_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_00c2a_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_00c2a_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_00c2a_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_00c2a_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_00c2a_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_00c2a_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_00c2a_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_00c2a_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_00c2a_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_00c2a_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_00c2a_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_00c2a_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00c2a_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_00c2a_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_00c2a_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_00c2a_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_00c2a_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_00c2a_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a4b39cbac0>"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 21 修改字体颜色\n",
    "\n",
    "<br>\n",
    "\n",
    "将 `salary` 列修改为红色字体"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "\r\n",
    "data.style.applymap(lambda x: \"color: red\", subset=['salary'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_62325_row0_col2, #T_62325_row1_col2, #T_62325_row2_col2, #T_62325_row3_col2, #T_62325_row4_col2, #T_62325_row5_col2, #T_62325_row6_col2, #T_62325_row7_col2, #T_62325_row8_col2, #T_62325_row9_col2, #T_62325_row10_col2, #T_62325_row11_col2, #T_62325_row12_col2, #T_62325_row13_col2, #T_62325_row14_col2, #T_62325_row15_col2, #T_62325_row16_col2, #T_62325_row17_col2, #T_62325_row18_col2, #T_62325_row19_col2 {\n",
       "  color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_62325_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_62325_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_62325_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_62325_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_62325_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_62325_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_62325_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_62325_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_62325_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_62325_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_62325_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_62325_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_62325_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_62325_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_62325_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_62325_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_62325_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_62325_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_62325_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_62325_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_62325_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_62325_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_62325_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_62325_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_62325_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_62325_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_62325_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_62325_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_62325_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_62325_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_62325_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_62325_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_62325_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_62325_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_62325_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_62325_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_62325_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_62325_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_62325_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_62325_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_62325_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_62325_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_62325_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_62325_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_62325_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_62325_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_62325_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_62325_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_62325_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_62325_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_62325_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_62325_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_62325_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_62325_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_62325_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_62325_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_62325_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_62325_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_62325_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_62325_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_62325_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_62325_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_62325_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_62325_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_62325_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_62325_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_62325_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_62325_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_62325_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_62325_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_62325_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_62325_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_62325_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_62325_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_62325_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_62325_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_62325_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_62325_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_62325_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_62325_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_62325_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_62325_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_62325_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_62325_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62325_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_62325_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_62325_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_62325_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_62325_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_62325_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a4b39de6d0>"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 22 修改背景颜色、对齐方式、字体大小\n",
    "<br>\n",
    "\n",
    "将整个 `dataframe` 进行如下设置：\n",
    "- 居中\n",
    "- 背景色修改为 `#F8F8FF`\n",
    "- 字体:13px"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "data.style.applymap(lambda x: \"font-size:13px; text-align:center; background-color:#f8f8ff\")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7cbc3_row0_col0, #T_7cbc3_row0_col1, #T_7cbc3_row0_col2, #T_7cbc3_row0_col3, #T_7cbc3_row0_col4, #T_7cbc3_row1_col0, #T_7cbc3_row1_col1, #T_7cbc3_row1_col2, #T_7cbc3_row1_col3, #T_7cbc3_row1_col4, #T_7cbc3_row2_col0, #T_7cbc3_row2_col1, #T_7cbc3_row2_col2, #T_7cbc3_row2_col3, #T_7cbc3_row2_col4, #T_7cbc3_row3_col0, #T_7cbc3_row3_col1, #T_7cbc3_row3_col2, #T_7cbc3_row3_col3, #T_7cbc3_row3_col4, #T_7cbc3_row4_col0, #T_7cbc3_row4_col1, #T_7cbc3_row4_col2, #T_7cbc3_row4_col3, #T_7cbc3_row4_col4, #T_7cbc3_row5_col0, #T_7cbc3_row5_col1, #T_7cbc3_row5_col2, #T_7cbc3_row5_col3, #T_7cbc3_row5_col4, #T_7cbc3_row6_col0, #T_7cbc3_row6_col1, #T_7cbc3_row6_col2, #T_7cbc3_row6_col3, #T_7cbc3_row6_col4, #T_7cbc3_row7_col0, #T_7cbc3_row7_col1, #T_7cbc3_row7_col2, #T_7cbc3_row7_col3, #T_7cbc3_row7_col4, #T_7cbc3_row8_col0, #T_7cbc3_row8_col1, #T_7cbc3_row8_col2, #T_7cbc3_row8_col3, #T_7cbc3_row8_col4, #T_7cbc3_row9_col0, #T_7cbc3_row9_col1, #T_7cbc3_row9_col2, #T_7cbc3_row9_col3, #T_7cbc3_row9_col4, #T_7cbc3_row10_col0, #T_7cbc3_row10_col1, #T_7cbc3_row10_col2, #T_7cbc3_row10_col3, #T_7cbc3_row10_col4, #T_7cbc3_row11_col0, #T_7cbc3_row11_col1, #T_7cbc3_row11_col2, #T_7cbc3_row11_col3, #T_7cbc3_row11_col4, #T_7cbc3_row12_col0, #T_7cbc3_row12_col1, #T_7cbc3_row12_col2, #T_7cbc3_row12_col3, #T_7cbc3_row12_col4, #T_7cbc3_row13_col0, #T_7cbc3_row13_col1, #T_7cbc3_row13_col2, #T_7cbc3_row13_col3, #T_7cbc3_row13_col4, #T_7cbc3_row14_col0, #T_7cbc3_row14_col1, #T_7cbc3_row14_col2, #T_7cbc3_row14_col3, #T_7cbc3_row14_col4, #T_7cbc3_row15_col0, #T_7cbc3_row15_col1, #T_7cbc3_row15_col2, #T_7cbc3_row15_col3, #T_7cbc3_row15_col4, #T_7cbc3_row16_col0, #T_7cbc3_row16_col1, #T_7cbc3_row16_col2, #T_7cbc3_row16_col3, #T_7cbc3_row16_col4, #T_7cbc3_row17_col0, #T_7cbc3_row17_col1, #T_7cbc3_row17_col2, #T_7cbc3_row17_col3, #T_7cbc3_row17_col4, #T_7cbc3_row18_col0, #T_7cbc3_row18_col1, #T_7cbc3_row18_col2, #T_7cbc3_row18_col3, #T_7cbc3_row18_col4, #T_7cbc3_row19_col0, #T_7cbc3_row19_col1, #T_7cbc3_row19_col2, #T_7cbc3_row19_col3, #T_7cbc3_row19_col4 {\n",
       "  font-size: 13px;\n",
       "  text-align: center;\n",
       "  background-color: #f8f8ff;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7cbc3_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7cbc3_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_7cbc3_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_7cbc3_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_7cbc3_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7cbc3_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_7cbc3_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_7cbc3_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_7cbc3_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7cbc3_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_7cbc3_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_7cbc3_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_7cbc3_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_7cbc3_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7cbc3_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_7cbc3_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_7cbc3_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_7cbc3_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_7cbc3_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7cbc3_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_7cbc3_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_7cbc3_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_7cbc3_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7cbc3_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_7cbc3_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_7cbc3_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_7cbc3_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_7cbc3_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_7cbc3_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_7cbc3_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_7cbc3_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_7cbc3_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_7cbc3_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_7cbc3_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_7cbc3_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_7cbc3_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7cbc3_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_7cbc3_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_7cbc3_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_7cbc3_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7cbc3_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_7cbc3_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_7cbc3_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_7cbc3_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_7cbc3_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_7cbc3_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_7cbc3_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_7cbc3_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_7cbc3_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_7cbc3_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_7cbc3_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_7cbc3_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_7cbc3_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_7cbc3_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_7cbc3_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_7cbc3_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_7cbc3_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_7cbc3_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_7cbc3_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_7cbc3_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_7cbc3_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_7cbc3_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_7cbc3_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_7cbc3_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_7cbc3_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_7cbc3_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_7cbc3_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_7cbc3_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_7cbc3_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_7cbc3_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_7cbc3_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_7cbc3_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_7cbc3_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_7cbc3_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_7cbc3_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_7cbc3_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_7cbc3_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_7cbc3_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_7cbc3_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_7cbc3_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_7cbc3_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_7cbc3_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_7cbc3_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_7cbc3_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7cbc3_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_7cbc3_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_7cbc3_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_7cbc3_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_7cbc3_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_7cbc3_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a49362bca0>"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 23 综合(链式)设置\n",
    "<br>\n",
    "\n",
    "除了上面的单个设置，还可以将多个设置进行结合，下面对整个 `dataframe` 进行如下设置：\n",
    "- 居中\n",
    "- 背景色修改为 `#F8F8FF`\n",
    "- 字体:13px\n",
    "\n",
    "并将 `salary` 列字体修改为红色"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "(data.style\r\n",
    "    .applymap(lambda x: \"font-size:13px; text-align:center; background-color:#f8f8ff\")\r\n",
    "    .applymap(lambda x: \"color: red\", subset=['salary'])\r\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_329cc_row0_col0, #T_329cc_row0_col1, #T_329cc_row0_col3, #T_329cc_row0_col4, #T_329cc_row1_col0, #T_329cc_row1_col1, #T_329cc_row1_col3, #T_329cc_row1_col4, #T_329cc_row2_col0, #T_329cc_row2_col1, #T_329cc_row2_col3, #T_329cc_row2_col4, #T_329cc_row3_col0, #T_329cc_row3_col1, #T_329cc_row3_col3, #T_329cc_row3_col4, #T_329cc_row4_col0, #T_329cc_row4_col1, #T_329cc_row4_col3, #T_329cc_row4_col4, #T_329cc_row5_col0, #T_329cc_row5_col1, #T_329cc_row5_col3, #T_329cc_row5_col4, #T_329cc_row6_col0, #T_329cc_row6_col1, #T_329cc_row6_col3, #T_329cc_row6_col4, #T_329cc_row7_col0, #T_329cc_row7_col1, #T_329cc_row7_col3, #T_329cc_row7_col4, #T_329cc_row8_col0, #T_329cc_row8_col1, #T_329cc_row8_col3, #T_329cc_row8_col4, #T_329cc_row9_col0, #T_329cc_row9_col1, #T_329cc_row9_col3, #T_329cc_row9_col4, #T_329cc_row10_col0, #T_329cc_row10_col1, #T_329cc_row10_col3, #T_329cc_row10_col4, #T_329cc_row11_col0, #T_329cc_row11_col1, #T_329cc_row11_col3, #T_329cc_row11_col4, #T_329cc_row12_col0, #T_329cc_row12_col1, #T_329cc_row12_col3, #T_329cc_row12_col4, #T_329cc_row13_col0, #T_329cc_row13_col1, #T_329cc_row13_col3, #T_329cc_row13_col4, #T_329cc_row14_col0, #T_329cc_row14_col1, #T_329cc_row14_col3, #T_329cc_row14_col4, #T_329cc_row15_col0, #T_329cc_row15_col1, #T_329cc_row15_col3, #T_329cc_row15_col4, #T_329cc_row16_col0, #T_329cc_row16_col1, #T_329cc_row16_col3, #T_329cc_row16_col4, #T_329cc_row17_col0, #T_329cc_row17_col1, #T_329cc_row17_col3, #T_329cc_row17_col4, #T_329cc_row18_col0, #T_329cc_row18_col1, #T_329cc_row18_col3, #T_329cc_row18_col4, #T_329cc_row19_col0, #T_329cc_row19_col1, #T_329cc_row19_col3, #T_329cc_row19_col4 {\n",
       "  font-size: 13px;\n",
       "  text-align: center;\n",
       "  background-color: #f8f8ff;\n",
       "}\n",
       "#T_329cc_row0_col2, #T_329cc_row1_col2, #T_329cc_row2_col2, #T_329cc_row3_col2, #T_329cc_row4_col2, #T_329cc_row5_col2, #T_329cc_row6_col2, #T_329cc_row7_col2, #T_329cc_row8_col2, #T_329cc_row9_col2, #T_329cc_row10_col2, #T_329cc_row11_col2, #T_329cc_row12_col2, #T_329cc_row13_col2, #T_329cc_row14_col2, #T_329cc_row15_col2, #T_329cc_row16_col2, #T_329cc_row17_col2, #T_329cc_row18_col2, #T_329cc_row19_col2 {\n",
       "  font-size: 13px;\n",
       "  text-align: center;\n",
       "  background-color: #f8f8ff;\n",
       "  color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_329cc_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_329cc_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_329cc_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_329cc_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_329cc_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_329cc_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_329cc_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_329cc_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_329cc_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_329cc_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_329cc_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_329cc_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_329cc_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_329cc_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_329cc_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_329cc_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_329cc_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_329cc_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_329cc_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_329cc_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_329cc_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_329cc_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_329cc_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_329cc_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_329cc_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_329cc_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_329cc_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_329cc_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_329cc_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_329cc_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_329cc_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_329cc_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_329cc_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_329cc_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_329cc_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_329cc_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_329cc_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_329cc_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_329cc_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_329cc_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_329cc_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_329cc_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_329cc_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_329cc_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_329cc_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_329cc_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_329cc_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_329cc_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_329cc_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_329cc_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_329cc_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_329cc_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_329cc_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_329cc_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_329cc_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_329cc_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_329cc_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_329cc_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_329cc_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_329cc_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_329cc_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_329cc_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_329cc_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_329cc_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_329cc_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_329cc_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_329cc_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_329cc_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_329cc_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_329cc_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_329cc_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_329cc_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_329cc_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_329cc_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_329cc_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_329cc_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_329cc_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_329cc_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_329cc_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_329cc_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_329cc_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_329cc_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_329cc_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_329cc_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_329cc_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_329cc_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_329cc_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_329cc_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_329cc_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_329cc_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a492f51850>"
      ]
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 24 导出样式"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "将上一题带有样式的 `pandas` 数据框导出为本地 Excel(`.xlsx`格式)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "source": [
    "(data.style\r\n",
    "    .applymap(lambda x: \"font-size:13px; text-align:center; background-color:#f8f8ff\")\r\n",
    "    .applymap(lambda x: \"color: red\", subset=['salary'])\r\n",
    "    .to_excel(\"./ttt.xlsx\")\r\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 25 制作指定列条形图\n",
    "\n",
    "<br>\n",
    "\n",
    "在 `pandas` 中对 `salary` 列使用条形图进行可视化，指定颜色`skyblue`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "source": [
    "data.style.bar(subset=['salary'], color='skyblue')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5315b_row0_col2, #T_5315b_row14_col2, #T_5315b_row16_col2, #T_5315b_row17_col2, #T_5315b_row19_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 60.2%, transparent 60.2%);\n",
       "}\n",
       "#T_5315b_row1_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 20.4%, transparent 20.4%);\n",
       "}\n",
       "#T_5315b_row2_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "}\n",
       "#T_5315b_row3_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 73.5%, transparent 73.5%);\n",
       "}\n",
       "#T_5315b_row4_col2, #T_5315b_row6_col2, #T_5315b_row10_col2, #T_5315b_row11_col2, #T_5315b_row13_col2, #T_5315b_row18_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 46.9%, transparent 46.9%);\n",
       "}\n",
       "#T_5315b_row5_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 82.3%, transparent 82.3%);\n",
       "}\n",
       "#T_5315b_row7_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 55.8%, transparent 55.8%);\n",
       "}\n",
       "#T_5315b_row8_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_5315b_row9_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 64.6%, transparent 64.6%);\n",
       "}\n",
       "#T_5315b_row12_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 29.2%, transparent 29.2%);\n",
       "}\n",
       "#T_5315b_row15_col2 {\n",
       "  width: 10em;\n",
       "  height: 80%;\n",
       "  background: linear-gradient(90deg,skyblue 42.5%, transparent 42.5%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5315b_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_5315b_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_5315b_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_5315b_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_5315b_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_5315b_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_5315b_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_5315b_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_5315b_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_5315b_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_5315b_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_5315b_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_5315b_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_5315b_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_5315b_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_5315b_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_5315b_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_5315b_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_5315b_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_5315b_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_5315b_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_5315b_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_5315b_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_5315b_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_5315b_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_5315b_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_5315b_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_5315b_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_5315b_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_5315b_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_5315b_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_5315b_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_5315b_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_5315b_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_5315b_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_5315b_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_5315b_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_5315b_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_5315b_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_5315b_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_5315b_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_5315b_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_5315b_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_5315b_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_5315b_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_5315b_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_5315b_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_5315b_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_5315b_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_5315b_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_5315b_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_5315b_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_5315b_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_5315b_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_5315b_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_5315b_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_5315b_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_5315b_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_5315b_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_5315b_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_5315b_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_5315b_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_5315b_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_5315b_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_5315b_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_5315b_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_5315b_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_5315b_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_5315b_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_5315b_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_5315b_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_5315b_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_5315b_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_5315b_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_5315b_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_5315b_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_5315b_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_5315b_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_5315b_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_5315b_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_5315b_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_5315b_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_5315b_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_5315b_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5315b_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_5315b_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_5315b_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_5315b_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_5315b_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_5315b_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a4b3cb9c70>"
      ]
     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 26 带有条件的样式（自定义样式）"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "将 `salary` 列数值大于 30000 的单元格字体修改为红色"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "source": [
    "(data.style\r\n",
    "    .applymap(lambda x: \"color: red\" if x > 30000 else \"\", subset=['salary'])\r\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_33f6e_row0_col2, #T_33f6e_row3_col2, #T_33f6e_row5_col2, #T_33f6e_row7_col2, #T_33f6e_row8_col2, #T_33f6e_row9_col2, #T_33f6e_row14_col2, #T_33f6e_row16_col2, #T_33f6e_row17_col2, #T_33f6e_row19_col2 {\n",
       "  color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_33f6e_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_33f6e_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_33f6e_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_33f6e_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_33f6e_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_33f6e_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_33f6e_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_33f6e_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_33f6e_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_33f6e_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_33f6e_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_33f6e_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_33f6e_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_33f6e_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_33f6e_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_33f6e_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_33f6e_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_33f6e_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_33f6e_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_33f6e_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_33f6e_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_33f6e_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_33f6e_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_33f6e_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_33f6e_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_33f6e_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_33f6e_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_33f6e_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_33f6e_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_33f6e_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_33f6e_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_33f6e_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_33f6e_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_33f6e_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_33f6e_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_33f6e_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_33f6e_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_33f6e_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_33f6e_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_33f6e_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_33f6e_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_33f6e_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_33f6e_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_33f6e_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_33f6e_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_33f6e_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_33f6e_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_33f6e_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_33f6e_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_33f6e_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_33f6e_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_33f6e_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_33f6e_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_33f6e_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_33f6e_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_33f6e_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_33f6e_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_33f6e_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_33f6e_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_33f6e_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_33f6e_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_33f6e_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_33f6e_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_33f6e_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_33f6e_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_33f6e_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_33f6e_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_33f6e_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_33f6e_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_33f6e_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_33f6e_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_33f6e_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_33f6e_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_33f6e_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_33f6e_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_33f6e_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_33f6e_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_33f6e_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_33f6e_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_33f6e_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_33f6e_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_33f6e_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_33f6e_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_33f6e_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33f6e_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_33f6e_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_33f6e_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_33f6e_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_33f6e_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_33f6e_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a4b3cb9d00>"
      ]
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 27 格式化输出日期类型\n",
    "\n",
    "<br>\n",
    "\n",
    "将 `createTime` 列格式化输出为 `xx年xx月xx日` "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "source": [
    "data.style.format( {\"createTime\": lambda x: x.strftime(\"%Y年%m月%d日\")} )"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_99ded_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_99ded_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_99ded_row0_col1\" class=\"data row0 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_99ded_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_99ded_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_99ded_row1_col1\" class=\"data row1 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_99ded_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_99ded_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_99ded_row2_col1\" class=\"data row2 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_99ded_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_99ded_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_99ded_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_99ded_row3_col1\" class=\"data row3 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_99ded_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_99ded_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_99ded_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_99ded_row4_col1\" class=\"data row4 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_99ded_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_99ded_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_99ded_row5_col1\" class=\"data row5 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_99ded_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_99ded_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_99ded_row6_col1\" class=\"data row6 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_99ded_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_99ded_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_99ded_row7_col1\" class=\"data row7 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_99ded_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_99ded_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_99ded_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_99ded_row8_col1\" class=\"data row8 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_99ded_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_99ded_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_99ded_row9_col1\" class=\"data row9 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_99ded_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_99ded_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_99ded_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_99ded_row10_col1\" class=\"data row10 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_99ded_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_99ded_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_99ded_row11_col1\" class=\"data row11 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_99ded_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_99ded_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_99ded_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_99ded_row12_col1\" class=\"data row12 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_99ded_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_99ded_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_99ded_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_99ded_row13_col1\" class=\"data row13 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_99ded_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_99ded_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_99ded_row14_col1\" class=\"data row14 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_99ded_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_99ded_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_99ded_row15_col1\" class=\"data row15 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_99ded_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_99ded_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_99ded_row16_col1\" class=\"data row16 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_99ded_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_99ded_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_99ded_row17_col1\" class=\"data row17 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_99ded_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_99ded_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_99ded_row18_col1\" class=\"data row18 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_99ded_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_99ded_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_99ded_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_99ded_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_99ded_row19_col1\" class=\"data row19 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_99ded_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_99ded_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_99ded_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x1a4b3d0da30>"
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     "metadata": {},
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  {
   "cell_type": "markdown",
   "source": [
    "### 28 指定（自定义）格式化数据\n",
    "\n",
    "<br>\n",
    "\n",
    "- 在 `salary` 列后增加\"元\"\n",
    "- 对 `matchScore` 列保留两位小数并增加\"分\"\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "source": [
    "data.style.format( {\"salary\": lambda x: str(x) + \" 元\", 'matchScore': '{:.2f}' })"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_5234d_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_5234d_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_5234d_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_5234d_row0_col2\" class=\"data row0 col2\" >37500 元</td>\n",
       "      <td id=\"T_5234d_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row0_col4\" class=\"data row0 col4\" >15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_5234d_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_5234d_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_5234d_row1_col2\" class=\"data row1 col2\" >15000 元</td>\n",
       "      <td id=\"T_5234d_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row1_col4\" class=\"data row1 col4\" >32.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_5234d_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_5234d_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_5234d_row2_col2\" class=\"data row2 col2\" >3500 元</td>\n",
       "      <td id=\"T_5234d_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_5234d_row2_col4\" class=\"data row2 col4\" >14.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_5234d_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_5234d_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_5234d_row3_col2\" class=\"data row3 col2\" >45000 元</td>\n",
       "      <td id=\"T_5234d_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_5234d_row3_col4\" class=\"data row3 col4\" >12.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_5234d_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_5234d_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_5234d_row4_col2\" class=\"data row4 col2\" >30000 元</td>\n",
       "      <td id=\"T_5234d_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row4_col4\" class=\"data row4 col4\" >12.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_5234d_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_5234d_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_5234d_row5_col2\" class=\"data row5 col2\" >50000 元</td>\n",
       "      <td id=\"T_5234d_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row5_col4\" class=\"data row5 col4\" >12.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_5234d_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_5234d_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_5234d_row6_col2\" class=\"data row6 col2\" >30000 元</td>\n",
       "      <td id=\"T_5234d_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row6_col4\" class=\"data row6 col4\" >12.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_5234d_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_5234d_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_5234d_row7_col2\" class=\"data row7 col2\" >35000 元</td>\n",
       "      <td id=\"T_5234d_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_5234d_row7_col4\" class=\"data row7 col4\" >3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_5234d_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_5234d_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_5234d_row8_col2\" class=\"data row8 col2\" >60000 元</td>\n",
       "      <td id=\"T_5234d_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row8_col4\" class=\"data row8 col4\" >1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_5234d_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_5234d_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_5234d_row9_col2\" class=\"data row9 col2\" >40000 元</td>\n",
       "      <td id=\"T_5234d_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_5234d_row9_col4\" class=\"data row9 col4\" >1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_5234d_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_5234d_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_5234d_row10_col2\" class=\"data row10 col2\" >30000 元</td>\n",
       "      <td id=\"T_5234d_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row10_col4\" class=\"data row10 col4\" >1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_5234d_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_5234d_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_5234d_row11_col2\" class=\"data row11 col2\" >30000 元</td>\n",
       "      <td id=\"T_5234d_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_5234d_row11_col4\" class=\"data row11 col4\" >4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_5234d_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_5234d_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_5234d_row12_col2\" class=\"data row12 col2\" >20000 元</td>\n",
       "      <td id=\"T_5234d_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_5234d_row12_col4\" class=\"data row12 col4\" >1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_5234d_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_5234d_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_5234d_row13_col2\" class=\"data row13 col2\" >30000 元</td>\n",
       "      <td id=\"T_5234d_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row13_col4\" class=\"data row13 col4\" >1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_5234d_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_5234d_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_5234d_row14_col2\" class=\"data row14 col2\" >37500 元</td>\n",
       "      <td id=\"T_5234d_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row14_col4\" class=\"data row14 col4\" >1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_5234d_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_5234d_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_5234d_row15_col2\" class=\"data row15 col2\" >27500 元</td>\n",
       "      <td id=\"T_5234d_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row15_col4\" class=\"data row15 col4\" >1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_5234d_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_5234d_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_5234d_row16_col2\" class=\"data row16 col2\" >37500 元</td>\n",
       "      <td id=\"T_5234d_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row16_col4\" class=\"data row16 col4\" >1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_5234d_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_5234d_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_5234d_row17_col2\" class=\"data row17 col2\" >37500 元</td>\n",
       "      <td id=\"T_5234d_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row17_col4\" class=\"data row17 col4\" >2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_5234d_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_5234d_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_5234d_row18_col2\" class=\"data row18 col2\" >30000 元</td>\n",
       "      <td id=\"T_5234d_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_5234d_row18_col4\" class=\"data row18 col4\" >3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5234d_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_5234d_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_5234d_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_5234d_row19_col2\" class=\"data row19 col2\" >37500 元</td>\n",
       "      <td id=\"T_5234d_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_5234d_row19_col4\" class=\"data row19 col4\" >0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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